{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lMde13VLDjiH",
    "outputId": "e75b5681-1267-457e-f384-45740d8aeea3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.2.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!pip install pytorch-metric-learning\n",
    "!pip install faiss-gpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 701,
     "referenced_widgets": [
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    },
    "id": "GJ_L0TrTDnEA",
    "outputId": "5f2b1a9b-2fd7-4414-9ee8-f9dfa79a7760"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "from pytorch_metric_learning import losses, testers\n",
    "from pytorch_metric_learning.utils.accuracy_calculator import AccuracyCalculator\n",
    "\n",
    "\n",
    "### MNIST code originally from https://github.com/pytorch/examples/blob/master/mnist/main.py ###\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 32, 3, 1)\n",
    "        self.conv2 = nn.Conv2d(32, 64, 3, 1)\n",
    "        self.dropout1 = nn.Dropout2d(0.25)\n",
    "        self.dropout2 = nn.Dropout2d(0.5)\n",
    "        self.fc1 = nn.Linear(9216, 128)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.conv2(x)\n",
    "        x = F.relu(x)\n",
    "        x = F.max_pool2d(x, 2)\n",
    "        x = self.dropout1(x)\n",
    "        x = torch.flatten(x, 1)\n",
    "        x = self.fc1(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "### MNIST code originally from https://github.com/pytorch/examples/blob/master/mnist/main.py ###\n",
    "def train(model, loss_func, device, train_loader, optimizer, loss_optimizer, epoch):\n",
    "    model.train()\n",
    "    for batch_idx, (data, labels) in enumerate(train_loader):\n",
    "        data, labels = data.to(device), labels.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        loss_optimizer.zero_grad()\n",
    "        embeddings = model(data)\n",
    "        loss = loss_func(embeddings, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        loss_optimizer.step()\n",
    "        if batch_idx % 100 == 0:\n",
    "            print(\"Epoch {} Iteration {}: Loss = {}\".format(epoch, batch_idx, loss))\n",
    "\n",
    "\n",
    "### convenient function from pytorch-metric-learning ###\n",
    "def get_all_embeddings(dataset, model):\n",
    "    tester = testers.BaseTester()\n",
    "    return tester.get_all_embeddings(dataset, model)\n",
    "\n",
    "\n",
    "### compute accuracy using AccuracyCalculator from pytorch-metric-learning ###\n",
    "def test(train_set, test_set, model, accuracy_calculator):\n",
    "    train_embeddings, train_labels = get_all_embeddings(train_set, model)\n",
    "    test_embeddings, test_labels = get_all_embeddings(test_set, model)\n",
    "    train_labels = train_labels.squeeze(1)\n",
    "    test_labels = test_labels.squeeze(1)\n",
    "    print(\"Computing accuracy\")\n",
    "    accuracies = accuracy_calculator.get_accuracy(\n",
    "        test_embeddings, test_labels, train_embeddings, train_labels, False\n",
    "    )\n",
    "    print(\"Test set accuracy (Precision@1) = {}\".format(accuracies[\"precision_at_1\"]))\n",
    "\n",
    "\n",
    "device = torch.device(\"cuda\")\n",
    "\n",
    "img_mean, img_std = (0.1307,), (0.3081,)\n",
    "\n",
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor(), transforms.Normalize(img_mean, img_std)]\n",
    ")\n",
    "\n",
    "batch_size = 64\n",
    "\n",
    "dataset1 = datasets.MNIST(\".\", train=True, download=True, transform=transform)\n",
    "dataset2 = datasets.MNIST(\".\", train=False, transform=transform)\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    dataset1, batch_size=batch_size, shuffle=True\n",
    ")\n",
    "test_loader = torch.utils.data.DataLoader(dataset2, batch_size=batch_size)\n",
    "\n",
    "model = Net().to(device)\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.01)\n",
    "num_epochs = 2\n",
    "\n",
    "\n",
    "### pytorch-metric-learning stuff ###\n",
    "loss_func = losses.SubCenterArcFaceLoss(num_classes=10, embedding_size=128).to(device)\n",
    "loss_optimizer = torch.optim.Adam(loss_func.parameters(), lr=1e-4)\n",
    "accuracy_calculator = AccuracyCalculator(include=(\"precision_at_1\",), k=1)\n",
    "### pytorch-metric-learning stuff ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for epoch in range(1, num_epochs + 1):\n",
    "    train(model, loss_func, device, train_loader, optimizer, loss_optimizer, epoch)\n",
    "    test(dataset1, dataset2, model, accuracy_calculator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1875/1875 [00:07<00:00, 246.32it/s]\n"
     ]
    }
   ],
   "source": [
    "train_embeddings, train_labels = get_all_embeddings(dataset1, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 144 outliers\n"
     ]
    }
   ],
   "source": [
    "outliers, _ = loss_func.get_outliers(train_embeddings, train_labels.squeeze(1))\n",
    "print(f\"There are {len(outliers)} outliers\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View some sample outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torchvision\n",
    "\n",
    "inv_normalize = transforms.Normalize(\n",
    "    mean=[-m / s for m, s in zip(img_mean, img_std)], std=[1 / s for s in img_std]\n",
    ")\n",
    "\n",
    "\n",
    "def imshow(img, figsize=(8, 4)):\n",
    "    img = inv_normalize(img)\n",
    "    npimg = img.numpy()\n",
    "    plt.figure(figsize=figsize)\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def imshow_many(dataset, outliers, n=32):\n",
    "    imgs = [\n",
    "        dataset[outliers[i]][0]\n",
    "        for i in np.random.choice(\n",
    "            len(outliers), size=min(n, len(outliers)), replace=False\n",
    "        )\n",
    "    ]\n",
    "    imshow(torchvision.utils.make_grid(imgs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's see what outliers are.\n",
    "# these are the samples that >threshold degrees aways from their dominant centers\n",
    "imshow_many(dataset1, outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "SubCenterArcFaceMNIST.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
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